cycombinepy.batch_correct

cycombinepy.batch_correct(adata, markers=None, batch_key='batch', label_key='cycombine_som', xdim=8, ydim=8, rlen=10, seed=473, n_clusters=None, norm_method='scale', ties_method='average', covar=None, anchor=None, ref_batch=None, parametric=True, out_layer='cycombine_corrected', copy=False, error_policy='raise', confound_policy='raise', return_report=False, uns_key='cycombinepy_correction')[source]

Run normalization, SOM clustering, and per-cluster ComBat.

Parameters:

  • adata (AnnData) – Input AnnData. adata.X is assumed to already be on an appropriate scale (e.g. post-asinh for cytometry).

  • markers (Optional[Iterable[str]]) – Var names to normalize/cluster/correct. Defaults to cycombinepy.get_markers().

  • batch_key (str) – Column in adata.obs holding batch assignments.

  • label_key (str) – Column in adata.obs to write cluster labels to.

  • xdim (Union[int, Sequence[int]]) – SOM grid dimensions. Sequences trigger iterative correction: for each (x, y) pair, re-normalize, re-cluster, and re-correct.

  • ydim (Union[int, Sequence[int]]) – SOM grid dimensions. Sequences trigger iterative correction: for each (x, y) pair, re-normalize, re-cluster, and re-correct.

  • rlen (int) – SOM training passes (forwarded to FlowSOM if supported).

  • seed (int) – FlowSOM random seed.

  • n_clusters (int | None) – If set, metacluster the SOM nodes into this many clusters.

  • norm_method (Literal['scale', 'rank', 'CLR', 'CLR_seu', 'CLR_med', 'qnorm', 'none']) – Normalization method used for clustering. See cycombinepy.normalize().

  • ties_method (Literal['average', 'min', 'max', 'dense', 'ordinal']) – Tie-breaking rule for norm_method="rank".

  • covar (str | None) – Forwarded to cycombinepy.correct_data().

  • anchor (str | None) – Forwarded to cycombinepy.correct_data().

  • ref_batch – Forwarded to cycombinepy.correct_data().

  • parametric (bool) – Forwarded to cycombinepy.correct_data().

  • out_layer (str) – Layer name to store the corrected matrix in.

  • copy (bool) – If True, return a corrected copy; otherwise mutate in place.

  • error_policy (Literal['raise', 'report', 'warn']) – Forwarded to cycombinepy.correct_data().

  • confound_policy (Literal['raise', 'skip', 'drop']) – Forwarded to cycombinepy.correct_data().

  • return_report (bool) – If True, return the aggregate batch orchestration report. With copy=True, returns (adata, report).

  • uns_key (str) – Key in adata.uns where the aggregate batch report is stored.

Return type:

AnnData | tuple[AnnData, dict] | dict | None